# Your search: "author:Daganzo, Carlos F."

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## Scholarly Works (77 results)

Time-dependent shortest path problems arise in a variety of applications; e.g., dynamic traffic assignment (DTA), network control, automobile driver guidance, ship routing and airplane dispatching. In the majority of cases one seeks the cheapest (least generalized cost) or quickest (least time) route between an origin and a destination for a given time of departure. This is the “forward” shortest path problem. In some applications, however, e.g., when dispatching airplanes from airports and in DTA versions of the “morning commute problem”, one seeks the cheapest or quickest routes for a given arrival time. This is the “backward” shortest path problem. It is shown that an algorithm that solves the forward quickest path problem on a network with first-in-first-out (FIFO) links also solves the backward quickest path problem on the same network. More generally, any algorithm that solves forward (or backward) problems of a particular type is shown also to solve backward (forward) problems of a conjugate type.

This paper proves that kinematic wave (KW) problems with concave (or convex) equations of state can be formulated as calculus of variations problems. Every well-posed problem of this type, no matter how complicated, is reduced to the determination of a shortest tree in a relevant region of spacetime where cost is predefined. A duality between KW theory and /least cost networks is thus unveiled. In the new formulation space-time curves that constrain flow, such as sets of moving bottlenecks, become space-time shortcuts. These shortcuts become part of the network and affect the nature of the solution but not the speed with which it can be obtained. Complex boundary conditions are naturally handled in the new formulation as constraints/shortcuts of this type.

This paper presents improved solution methods for kinematic wave trafficc problems with concave flow-density relations. As explained in part I of this work, the solution of a kinematic wave problem is a set of continuum least-cost paths in space-time. The least cost to reach a point is the vehicle number. The idea here consists in overlaying a dense but discrete network with appropriate costs in the solution region and then using a shortest-path algorithm to estimate vehicle numbers. With properly designed networks, this procedure is more accurate than existing methods and can be applied to more complicated problems. In many important cases its results are exact.

Bus schedules cannot be easily maintained on busy lines with short headways: Experience shows that buses offering this type of service usually arrive irregularly at their stops, often in bunches. Although transit agencies build slack into their schedules to alleviate this problem, their attempts often fail because practical amounts of slack cannot prevent large localized disruptions from spreading system-wide. This paper describes a more resilient control scheme that overcomes this problem. The method also produces even headways with less slack than the conventional approach. Thus, buses can run faster and be more productive.

This paper proposes a macroscopic behavioral theory of traffic dynamics for homogeneous, multi-lane freeways. The theory makes predictions for separate groups of lanes while recognizing that the traffic stream is usually composed of aggressive and timid drivers. Its principles are so simple that non-scientist drivers can understand them. The simplest version of the theory, which is described in its full complexity without calculus, is shown to be qualitatively consistent with experimental observations, including the most puzzling. Its predictions agree with the following phenomena: (i) the 'reversed lambda' pattern frequently observed in scatter-plots of flow versus occupancy and the lane-specific evolution of the data points with time, including the 'hysteresis' phenomenon, (ii) the lane-specific patterns in the time series of speed (and flow) in both queued and unqueued traffic, and (iii) the peculiar ways in which disturbances of various types propagate across detector stations. The latter effects include the evolution of both, stoppages and transitions between the queued and unqueued traffic regimes. The simple model is specified by means of eight observable parameters. The paper gives a recipe for solving any well-posed problem with this model and does so in sufficient detail to allow the development of computer models. A few approaches and possible generalizations are suggested. A sequel to this paper, devoted to freeway sections near on-ramps, will attempt to explain in more detail than previously attempted how queuing begins at merges.

This paper describes the network shapes and operating characteristics that allow a transit system to deliver a level of service competitive with that of the automobile. To provide exhaustive results for service regions of different sizes and demographics, the paper idealizes these regions as squares, and their possible networks with a broad and realistic family that combines the grid and the hub-and-spoke concepts. The paper also shows how to use these results to generate master plans for transit systems of real cities.

The analysis reveals which network structure and technology (Bus, BRT or Metro) delivers the desired performance with the least cost. It is found that the more expensive the system’s infrastructure the more it should tilt toward the hub-and-spoke concept. Both, Bus and BRT systems outperform Metro, even for large dense cities. And BRT competes effectively with the automobile unless a city is big and its demand low. Agency costs are always small compared with user costs; and both decline with the demand density. In all cases, increasing the spatial concentration of stops beyond a critical level increases both, the user and agency costs. Too much spatial coverage is counterproductive.

This paper describes the network shapes and operating characteristics that allow a transit system to deliver a level of service competitive with that of the automobile. To provide exhaustive results for service regions of different sizes and demographics, the paper idealizes these regions as squares, and their possible networks with a broad and realistic family that combines the grid and the hub-and-spoke concepts. The paper also shows how to use these results to generate master plans for transit systems of real cities. The analysis reveals which network structure and technology (Bus, BRT or Metro) delivers the desired performance with the least cost. It is found that the more expensive the system’s infrastructure the more it should tilt toward the hub-and-spoke concept. Both, Bus and BRT systems outperform Metro, even for large dense cities. And BRT competes effectively with the automobile unless a city is big and its demand low. Agency costs are always small compared with user costs; and both decline with the demand density. In all cases, increasing the spatial concentration of stops beyond a critical level increases both, the user and agency costs. Too much spatial coverage is counterproductive.